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import os |
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from pathlib import Path |
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from typing import Any, Callable, Dict, List, Optional, Tuple, Union |
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from torch import Tensor |
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from .folder import find_classes, make_dataset |
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from .video_utils import VideoClips |
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from .vision import VisionDataset |
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class UCF101(VisionDataset): |
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""" |
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`UCF101 <https://www.crcv.ucf.edu/data/UCF101.php>`_ dataset. |
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UCF101 is an action recognition video dataset. |
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This dataset consider every video as a collection of video clips of fixed size, specified |
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by ``frames_per_clip``, where the step in frames between each clip is given by |
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``step_between_clips``. The dataset itself can be downloaded from the dataset website; |
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annotations that ``annotation_path`` should be pointing to can be downloaded from `here |
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<https://www.crcv.ucf.edu/data/UCF101/UCF101TrainTestSplits-RecognitionTask.zip>`_. |
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To give an example, for 2 videos with 10 and 15 frames respectively, if ``frames_per_clip=5`` |
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and ``step_between_clips=5``, the dataset size will be (2 + 3) = 5, where the first two |
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elements will come from video 1, and the next three elements from video 2. |
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Note that we drop clips which do not have exactly ``frames_per_clip`` elements, so not all |
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frames in a video might be present. |
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Internally, it uses a VideoClips object to handle clip creation. |
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Args: |
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root (str or ``pathlib.Path``): Root directory of the UCF101 Dataset. |
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annotation_path (str): path to the folder containing the split files; |
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see docstring above for download instructions of these files |
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frames_per_clip (int): number of frames in a clip. |
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step_between_clips (int, optional): number of frames between each clip. |
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fold (int, optional): which fold to use. Should be between 1 and 3. |
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train (bool, optional): if ``True``, creates a dataset from the train split, |
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otherwise from the ``test`` split. |
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transform (callable, optional): A function/transform that takes in a TxHxWxC video |
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and returns a transformed version. |
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output_format (str, optional): The format of the output video tensors (before transforms). |
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Can be either "THWC" (default) or "TCHW". |
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Returns: |
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tuple: A 3-tuple with the following entries: |
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- video (Tensor[T, H, W, C] or Tensor[T, C, H, W]): The `T` video frames |
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- audio(Tensor[K, L]): the audio frames, where `K` is the number of channels |
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and `L` is the number of points |
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- label (int): class of the video clip |
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""" |
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def __init__( |
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self, |
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root: Union[str, Path], |
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annotation_path: str, |
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frames_per_clip: int, |
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step_between_clips: int = 1, |
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frame_rate: Optional[int] = None, |
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fold: int = 1, |
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train: bool = True, |
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transform: Optional[Callable] = None, |
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_precomputed_metadata: Optional[Dict[str, Any]] = None, |
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num_workers: int = 1, |
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_video_width: int = 0, |
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_video_height: int = 0, |
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_video_min_dimension: int = 0, |
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_audio_samples: int = 0, |
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output_format: str = "THWC", |
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) -> None: |
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super().__init__(root) |
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if not 1 <= fold <= 3: |
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raise ValueError(f"fold should be between 1 and 3, got {fold}") |
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extensions = ("avi",) |
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self.fold = fold |
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self.train = train |
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self.classes, class_to_idx = find_classes(self.root) |
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self.samples = make_dataset(self.root, class_to_idx, extensions, is_valid_file=None) |
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video_list = [x[0] for x in self.samples] |
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video_clips = VideoClips( |
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video_list, |
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frames_per_clip, |
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step_between_clips, |
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frame_rate, |
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_precomputed_metadata, |
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num_workers=num_workers, |
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_video_width=_video_width, |
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_video_height=_video_height, |
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_video_min_dimension=_video_min_dimension, |
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_audio_samples=_audio_samples, |
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output_format=output_format, |
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) |
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self.full_video_clips = video_clips |
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self.indices = self._select_fold(video_list, annotation_path, fold, train) |
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self.video_clips = video_clips.subset(self.indices) |
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self.transform = transform |
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@property |
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def metadata(self) -> Dict[str, Any]: |
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return self.full_video_clips.metadata |
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def _select_fold(self, video_list: List[str], annotation_path: str, fold: int, train: bool) -> List[int]: |
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name = "train" if train else "test" |
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name = f"{name}list{fold:02d}.txt" |
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f = os.path.join(annotation_path, name) |
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selected_files = set() |
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with open(f) as fid: |
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data = fid.readlines() |
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data = [x.strip().split(" ")[0] for x in data] |
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data = [os.path.join(self.root, *x.split("/")) for x in data] |
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selected_files.update(data) |
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indices = [i for i in range(len(video_list)) if video_list[i] in selected_files] |
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return indices |
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def __len__(self) -> int: |
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return self.video_clips.num_clips() |
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def __getitem__(self, idx: int) -> Tuple[Tensor, Tensor, int]: |
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video, audio, info, video_idx = self.video_clips.get_clip(idx) |
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label = self.samples[self.indices[video_idx]][1] |
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if self.transform is not None: |
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video = self.transform(video) |
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return video, audio, label |
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